Before the advent of neural networks, text simplification approaches performed each operation separately in a pipeline manner using either handcrafted rules [1]}, [2]}, [3]} or data-driven methods based on parallel corpora [4]}, [5]}, [6]}. Following neural machine translation, the trend changed to performing all the operations together end-to-end [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]} at the cost of controllability and performance as shown in this paper.
